Text Generation
Transformers
Safetensors
English
gemma4
uncensored
abliterated
forensics
thinking
reasoning
2026
Instructions to use DreamFast/Gemma4-e4b-abliterlitics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DreamFast/Gemma4-e4b-abliterlitics with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DreamFast/Gemma4-e4b-abliterlitics")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DreamFast/Gemma4-e4b-abliterlitics", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DreamFast/Gemma4-e4b-abliterlitics with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DreamFast/Gemma4-e4b-abliterlitics" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DreamFast/Gemma4-e4b-abliterlitics", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DreamFast/Gemma4-e4b-abliterlitics
- SGLang
How to use DreamFast/Gemma4-e4b-abliterlitics with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "DreamFast/Gemma4-e4b-abliterlitics" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DreamFast/Gemma4-e4b-abliterlitics", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "DreamFast/Gemma4-e4b-abliterlitics" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DreamFast/Gemma4-e4b-abliterlitics", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DreamFast/Gemma4-e4b-abliterlitics with Docker Model Runner:
docker model run hf.co/DreamFast/Gemma4-e4b-abliterlitics
Upload report/provenance_report.md with huggingface_hub
Browse files- report/provenance_report.md +83 -0
report/provenance_report.md
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# Forensic Provenance Investigation — BaseCS / Apostate Qwen3-4B
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**Date:** 2026-07-10 05:20 UTC
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**Question:** Was BaseCS's Qwen3-4B abliterated model built on top of the Apostate abliterated model?
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---
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## Executive Summary
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**Verdict: INCONCLUSIVE — Evidence is ambiguous.**
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The original Apostate model shows a striking 0.966 median cosine similarity with BaseCS on shared modified tensors. However, fresh Apostate runs show this tool is **highly non-deterministic**, and some fresh runs also achieve moderate-to-high cosine (0.82) against BaseCS. The stacking hypothesis (BaseCS built on Apostate) cannot be definitively proven or disproven with weight analysis alone.
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---
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## 1. Apostate Tool Determinism
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Four fresh Apostate runs (apostate_1 through apostate_4) were created from the same base model (Qwen3-4B-Instruct-2507) to establish whether the Apostate tool produces deterministic outputs.
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### 1.1 Modification Scope
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| Model | Changed Tensors | Tensor Types | Layers | Layer Coverage |
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|---|---|---|---|---|
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**Finding:** Apostate is **NOT deterministic**. Tensor counts range from 45 to 62, layer coverage from 77.8% to 94.4%. The original apostate modified 57 tensors — within the new-run range but not exactly reproducible.
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### 1.2 Inter-Variant Cosine Similarity
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| Pair | Overlap Keys | Non-trivial Cosine (mean) |
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|---|---|---|
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**Finding:** Inter-apostate cosine varies from **0.13 to 0.9995**. Apostates 2 and 3 are nearly identical (0.9995), but apostate 1 is almost orthogonal to everything (~0.15). This establishes the **Apostate variance band**: cosine between independent runs can range from near-zero to near-perfect.
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---
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## 2. Apostate vs BaseCS: Overlap Reproducibility
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If BaseCS built on the original Apostate, only the original apostate should show high cosine with BaseCS. If the overlap is structural (shared refusal direction), fresh apostates should show similar cosine.
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| Model vs BaseCS | Mean Cosine | Median Cosine | Overlap Keys |
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|---|---|---|---|
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**Finding:** The original apostate (0.966 median) has the **highest** cosine with BaseCS, but apostates 2 (0.825) and 3 (0.824) also show high overlap. Apostate 1 (0.041) and apostate 4 (0.459) show low-to-moderate overlap.
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Since apostates 2/3 achieve cos ~0.82 independently, the original's 0.966 is **within the range of plausible Apostate variance** — it's higher but not categorically different from what independent runs can produce.
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---
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## 3. Stacking Hypothesis Test
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If `Base = Apostate + ExtraEdits`, then on the 57 shared modified tensors:
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- `cos(D_apostate, D_base)` should be very high (edit directions match)
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- `||base - apostate|| / ||base - base||` should be <1 (residual is smaller than full edit)
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- Regression slope `D_base ≈ α·D_apostate` should be ~1.0
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- R² should be high (apostate edits explain most base edit variance)
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---
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## 4. Conclusion
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### Evidence FOR stacking (BaseCS built on Apostate)
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1. **Original apostate has the highest cosine (0.966)** — higher than any fresh apostate
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2. **Bimodal distribution** — 50/57 tensors have cos > 0.9, 0 in the middle range. This isn't gradual overlap; it's near-perfect on the main tensors.
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3. **Regression slope ~1.06** with R² ~0.93 — Apostate edits explain 93% of BaseCS edit variance on shared tensors
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4. **Residual ratio ~0.31** — `||base - apostate||` is only 31% of `||base - base||` on shared tensors, meaning ~69% of BaseCS's edit is already present in Apostate
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5. **Apostate's 57 modified tensors are a strict subset** of BaseCS's 253
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### Evidence AGAINST stacking / For independent development
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1. **Apostate is non-deterministic** — cosine between independent runs ranges from 0.13 to 0.9995. The high cosine could reflect that some Apostate configurations naturally converge on similar refusal directions.
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2. **Fresh apostates 2 and 3 also achieve cos ~0.82** against BaseCS — not as high as the original's 0.966, but still substantial, suggesting structural overlap.
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3. **Zero exact/near-exact matches** — no tensor in BaseCS is bitwise or near-bitwise identical to Apostate. If stacking occurred, unchanged tensors should match (modulo GGUF noise).
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4. **GGUF noise confound** — BaseCS went through a GGUF quantization round-trip (BF16→Q8_0→BF16 or similar), which introduces quantization noise. This could mask exact matches even if stacking occurred, OR could create the appearance of similarity where none exists.
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5. **The 7 low-cosine outlier tensors** (layers 3-6, 33-35) suggest BaseCS's edit direction diverges from Apostate on early/late layers, which is inconsistent with simple stacking.
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### Assessment
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| Verdict | **INCONCLUSIVE** |
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|---|---|
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| Best explanation | BaseCS likely used a **similar refusal-direction identification method** (possibly influenced by or derived from Apostate's approach), applied independently with different scope and hyperparameters. The near-perfect cosine on mid-layer tensors is consistent with both stacking AND convergent methodology. |
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| Probability of direct stacking | **40-60%** — The evidence is suggestive but not conclusive. The 0.966 cosine exceeds the fresh-apostate baseline (~0.82 max), but the non-deterministic nature of Apostate and the GGUF noise confound prevent a definitive ruling. |
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| Recommended follow-up | 1) Check BaseCS's model card/public statements for attribution. 2) Reproduce the GGUF round-trip on the original Apostate to measure the noise floor. 3) Compare timestamps and publication dates. |
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